CN103020328B - Optimum design method for small antenna - Google Patents
Optimum design method for small antenna Download PDFInfo
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- CN103020328B CN103020328B CN201110279343.2A CN201110279343A CN103020328B CN 103020328 B CN103020328 B CN 103020328B CN 201110279343 A CN201110279343 A CN 201110279343A CN 103020328 B CN103020328 B CN 103020328B
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Abstract
The invention relates to an optimum design method for a small antenna. The method comprises the following steps: presetting the value ranges of characteristic parameters of the small antenna according to the design requirement of the small antenna; selecting a plurality of sampling points within the preset value ranges of the characteristic parameters to obtain a characteristic parameter sample set; presetting an appropriate fitness function according to an optimizing index; and searching the optimum characteristic parameter value within the value ranges of the characteristic parameters by an optimization algorithm so as to maximize the obtained fitness function value. According to the method disclosed by the invention, the automatic searching of the optimum characteristic parameter value of the small antenna is realized through presetting the fitness function and adopting the optimization algorithm so as to acquire an optimum structure parameter value of the small antenna, which can meet the design requirement, the whole design process does not need manual intervention, the design efficiency is greatly improved, a mass of computation is avoided, the design cost, particularly manpower, material and time cost can be reduced, and the automation of small antenna design is realized.
Description
Technical field
The present invention relates to antenna design techniques, more particularly, relate to a kind of optimum design method for small antenna.
Background technology
Carry out antenna particularly miniature antenna design time, how determining the physical dimension of miniature antenna, is an important step in Antenna Design process.The characteristic information of small antenna structure is described by one group of parameter, as the thickness, specific inductive capacity etc. of metal live width, substrate.In order to obtain the electromagnetic response of expectation: such as, at 2.4GHz frequency place, the value of electromagnetic response parameter S11 should, lower than-10dB, need to do a large amount of experiments can find suitable miniature antenna characteristic ginseng value usually.
Traditional miniature antenna method for designing is, change each property parameters value of miniature antenna manually one by one, test the electromagnetic response that this structure is corresponding, and respond with target electromagnetic and contrast, so constantly circulation, finally finds and responds metamaterial modular construction property parameters the most close with target electromagnetic.The structural parameters of adjustment miniature antenna are steps very consuming time, in order to reach miniature antenna designing requirement and specific electromagnetic response, each structural parameters for miniature antenna are finely tuned, its fine setting unit may reach grade, workload is very large, not only needs manual intervention but also cannot automatically realize.And huge workload makes there is great requirement to human and material resources, time, there is vital effect the time that therefore how to shorten to raising miniature antenna design efficiency.
Summary of the invention
The technical problem to be solved in the present invention is, not only needs manual intervention and defect that is consuming time, effort, provide a kind of optimum design method for small antenna that can improve miniature antenna design efficiency when the above-mentioned miniature antenna for prior art designs.
The technical solution adopted for the present invention to solve the technical problems is: construct a kind of optimum design method for small antenna, comprise step:
Design requirement according to miniature antenna presets the characteristic parameter span of miniature antenna;
In the characteristic parameter span preset, choose multiple sampled point, obtain characteristic parameter sample set;
Suitable fitness function is preset according to optimizing index;
Adopt optimization algorithm to search for optimal characteristics parameter value in described characteristic parameter span, make described fitness function value maximum.
In optimum design method for small antenna of the present invention, described miniature antenna comprises substrate and is attached to the metal wire on substrate.
In optimum design method for small antenna of the present invention, described characteristic parameter comprises specific inductive capacity, metal wire live width, substrate thickness.
In optimum design method for small antenna of the present invention, described metal wire is copper cash, aluminum steel or silver-colored line.
In optimum design method for small antenna of the present invention, described optimizing index comprises electromagnetic response characteristic.
In optimum design method for small antenna of the present invention, described electromagnetic response characteristic comprises scattering parameter, and described scattering parameter comprises transmission coefficient, reflection coefficient.
In optimum design method for small antenna of the present invention, described optimization algorithm is particle swarm optimization algorithm.
In optimum design method for small antenna of the present invention, described optimization algorithm is genetic algorithm or simulated annealing.
In optimum design method for small antenna of the present invention, adopt optimization algorithm search characteristics parameter in described characteristic parameter sample set, make described fitness function maximum, specifically comprise step:
Calculate the fitness function value that each characteristic parameter sample is corresponding, obtain the characteristic parameter sample that fitness function value is maximum;
Fitness function value according to each characteristic parameter sample and correspondence thereof produces new characteristic parameter sample set, and described new characteristic parameter sample set is included in default characteristic parameter span;
Detect and whether meet iteration termination condition, if so, then terminate search; If not, then continue iterative search, until meet iteration termination condition.
In optimum design method for small antenna of the present invention, described iteration termination condition is for reaching preset search number of times.
Implement technical scheme of the present invention, there is following beneficial effect: the present invention is by default fitness function and adopt optimization algorithm to realize the automatic search of miniature antenna characteristic parameter, thus get the structural parameters of the miniature antenna of the optimum that can meet design requirement, whole design process is without the need to manual intervention, and substantially increase design efficiency, avoid a large amount of calculating, decrease design cost, particularly human and material resources and time cost, fully achieves the robotization of miniature antenna design.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described, in accompanying drawing:
Fig. 1 is the schematic flow sheet of the optimum design method for small antenna according to one embodiment of the invention;
Fig. 2 is the detailed process schematic diagram of the step 104 shown in Fig. 1.
Embodiment
Fig. 1 is the schematic flow sheet of the optimum design method for small antenna according to one embodiment of the invention, and optimum design method for small antenna 100 comprises the steps:
Step 101, the design requirement according to miniature antenna presets the scope of the characteristic parameter of miniature antenna.Here design requirement can be locus of such as miniature antenna placement etc.Miniature antenna generally, comprises substrate and is attached to the metal wire on substrate.Metal wire can be copper cash, aluminum steel or silver-colored line.Characteristic parameter comprises size or the characteristic of miniature antenna, such as specific inductive capacity, metal wire live width, substrate thickness etc.Usually, the locus that miniature antenna is placed determines the approximate dimensions scope of miniature antenna.
Step 102, chooses multiple sampled point, obtains characteristic parameter sample set within the scope of the characteristic parameter preset.Concrete selection principle does not limit, such as, can be uniform design, Stochastic choice.The quantity of sampled point is also according to needing to set.Generally, the quantity of sampled point is more, and degree of accuracy will be higher, but computation complexity also can correspondingly increase.
Step 103, presets suitable fitness function according to the optimizing index of miniature antenna.Here optimizing index can be any index demand, and can be such as electromagnetic response characteristic, electromagnetic response characteristic comprises scattering parameter S, and scattering parameter S comprises reflection coefficient S11, transmission coefficient S12 etc.Suitable fitness function can obtain good optimum results fast, and reaches optimizing index as much as possible.Illustrate below: in radio frequency applications, suppose that designing requirement is, at 2.4GHz frequency place, the value of reflection coefficient S11 is more low better, so can preset fitness function y (g)=exp (-S (g)) according to this requirement, wherein g is the characteristic ginseng value of miniature antenna, and S (g) representation feature parameter value is that the reflection coefficient S11 that the miniature antenna of g is corresponding at 2.4GHz frequency place, reflection coefficient S11 can be obtained by CST emulation.
Above-mentioned example only for illustration of the selection cause of fitness function, not as limitation of the present invention.Except reflection coefficient S11, can also be other index characterization parameter, this determines according to design requirement completely, and the parameter paid close attention to of different application scenarios also can difference to some extent.
Step 104, adopts optimization algorithm in characteristic parameter span, search for optimum characteristic ginseng value, makes fitness function value maximum.The maximum characteristic parameter of such fitness function value is exactly the design parameter of the miniature antenna that we need.
Optimization algorithm can be that any can realization searches for optimum algorithm, such as particle swarm optimization algorithm, genetic algorithm, simulated annealing, neural network or ant algorithm, the particular content of above-mentioned various algorithm can see correlation technique data, here do not elaborate, only it is simply introduced.
Genetic algorithm: according to organic evolution, the algorithm that the chromosomal selection of gene in simulation evolutionary process, crossover and mutation obtain.During evolution, good individuality has larger chances of survival.
Simulated annealing: the crystallization process being solid matter in simulation statistical physics.In the process of annealing, if the solution searched accepts; Otherwise, accept bad solution (namely realizing the thought of variation or variation) with certain probability, reach the object jumping out locally optimal solution.
Neural network: the process of simulation cerebral nerve process, by each neuronic competition and cooperation, realizes the process selected and make a variation.
Ant algorithm: the behavior of simulation ant, anthropomorphic plan thing, the cooperation mode to ant learns.
In an embodiment of the present invention, what optimization algorithm adopted is particle swarm optimization algorithm.As shown in Figure 2, step 104 specifically comprises the steps:
Step 1041, calculates the fitness function value that each characteristic parameter sample is corresponding, obtains the characteristic parameter sample that fitness function value is maximum.
For characteristic parameter sample g
i, (i is value between 1 and N, and N is the element number of characteristic parameter sample set, namely the quantity of sampled point), its speed of initialization is v
i, such as, can set v
i=0.1*g
i.For each characteristic parameter sample g
i, calculate its fitness function value y (g
i), find maximum fitness function value, the sample that maximum fitness function value is corresponding is designated as g
b.
Step 1042, the fitness function value according to each characteristic parameter sample and correspondence thereof produces new characteristic parameter sample set, and new characteristic parameter sample set is included in the scope of default characteristic parameter.
For each sample g
i, upgrade sample speed in order to lower equation:
v
i＝c
0×v
i+c
1×rand×(pb
i-g
i)+c
2×rand×(g
b-g
i)；
Wherein, c
0, c
1, c
2for constant, such as c
0=0.5, c
1=2, c
2=2, rand is the equally distributed random number between 0 and 1, pb
irepresent the local best points in i-th sample searches course in iterative search procedures.Sample position is upgraded again in order to lower equation:
g
i＝g
i+v
i。
Step 1043, detects whether meet iteration termination condition, if so, then g
bbe the sample that optimum fitness function value is corresponding, terminate search; If not, then return step 1041 and continue iterative search, until meet iteration termination condition.
Here iteration termination condition can set according to actual demand.Such as can be set as reaching default searching times (such as 1000 times); Also can be the g when continuous several times iteration exports
bvalue difference different very little, also namely in certain deviation range, so can think reached terminate search condition.
Method for designing of the present invention is very suitable for miniature antenna, is especially applicable to the Automation Design of planar microstrip small antenna structure.The present invention is by default fitness function and adopt optimization algorithm to realize the automatic search of miniature antenna characteristic parameter, thus get the structural parameters of the miniature antenna of the optimum that can meet design requirement, whole design process is without the need to manual intervention, and substantially increase design efficiency, avoid a large amount of calculating, decrease design cost, particularly human and material resources and time cost, fully achieve the robotization of miniature antenna design.
By reference to the accompanying drawings embodiments of the invention are described above; but the present invention is not limited to above-mentioned embodiment; above-mentioned embodiment is only schematic; instead of it is restrictive; those of ordinary skill in the art is under enlightenment of the present invention; do not departing under the ambit that present inventive concept and claim protect, also can make a lot of form, these all belong within protection of the present invention.
Claims (6)
1. an optimum design method for small antenna, is characterized in that, comprises step:
Design requirement according to miniature antenna presets the span of the characteristic parameter of miniature antenna;
In the characteristic parameter span preset, choose multiple sampled point, obtain characteristic parameter sample set;
Suitable fitness function is preset according to optimizing index;
Adopt optimization algorithm to search for optimal characteristics parameter value in described characteristic parameter span, make described fitness function value maximum;
Wherein, described optimization algorithm is particle swarm optimization algorithm, and described employing optimization algorithm searches for optimal characteristics parameter value in described characteristic parameter span, and the maximum step of described fitness function value is specifically comprised:
Calculate the fitness function value that each characteristic parameter sample is corresponding, obtain the characteristic parameter sample that fitness function value is maximum;
Fitness function value according to each characteristic parameter sample and correspondence thereof produces new characteristic parameter sample set, and described new characteristic parameter sample set is included in default characteristic parameter span;
Detect and whether meet iteration termination condition, if so, then terminate search; If not, then continue iterative search, until meet iteration termination condition; Described iteration termination condition is for reaching preset search number of times.
2. optimum design method for small antenna according to claim 1, is characterized in that, described miniature antenna comprises substrate and is attached to the metal wire on substrate.
3. optimum design method for small antenna according to claim 2, is characterized in that, described characteristic parameter comprises specific inductive capacity, metal wire live width, substrate thickness.
4. optimum design method for small antenna according to claim 2, is characterized in that, described metal wire is copper cash, aluminum steel or silver-colored line.
5. optimum design method for small antenna according to claim 1, is characterized in that, described optimizing index comprises electromagnetic response characteristic.
6. optimum design method for small antenna according to claim 5, is characterized in that, described electromagnetic response characteristic comprises scattering parameter, and described scattering parameter comprises transmission coefficient, reflection coefficient.
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| CN104735707B (en) * | 2013-12-24 | 2019-07-05 | 中国移动通信集团广东有限公司 | A kind of failure antenna positioning method, device and electronic equipment |
| CN105677988A (en) * | 2016-01-06 | 2016-06-15 | 安徽工程大学 | Method for rapidly optimizing simulation design of antenna |
| CN107946750B (en) * | 2016-10-13 | 2019-08-16 | 大唐移动通信设备有限公司 | A kind of method and device of the mode of grooving of determining antenna patch |
| CN107704673B (en) * | 2017-09-26 | 2021-01-15 | 中国人民解放军空军工程大学 | Rapid design method for broadband coding metamaterial |
| CN109117545B (en) * | 2018-08-07 | 2022-03-11 | 中南大学 | Neural network-based antenna rapid design method |
| CN116611273A (en) * | 2023-07-20 | 2023-08-18 | 深圳飞骧科技股份有限公司 | Optimized design method, system and related equipment for broadband high-gain transmission array antenna |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101267062A (en) * | 2008-04-30 | 2008-09-17 | 西安电子科技大学 | Method for predicting antenna electric performance based on simulated distortion reflective side |
| CN102156761A (en) * | 2010-12-01 | 2011-08-17 | 北京邮电大学 | Quick simulation and optimization method for microwave radio frequency device |
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| CN101267062A (en) * | 2008-04-30 | 2008-09-17 | 西安电子科技大学 | Method for predicting antenna electric performance based on simulated distortion reflective side |
| CN102156761A (en) * | 2010-12-01 | 2011-08-17 | 北京邮电大学 | Quick simulation and optimization method for microwave radio frequency device |
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